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NEW QUESTION: 1
ノードとノードグループの関係に関して、次のうちどれが当てはまりますか？
A. ノードグループによって管理されているノードは削除できません。
B. すべてのノードは、最初はノードグループに関連付けられていません。
C. すべてのノードは、最初はデフォルトのノードグループに関連付けられています。
D. ノードは1つのノードグループにのみ属することができます。
Answer: C
Explanation:
D：ノードはノードグループのメンバーである必要があります。最初は、すべてのApplication ServerノードはデフォルトのDefaultNodeGroupノードグループのメンバーです。
注：ノードグループは、管理対象ノードの集合です。
NEW QUESTION: 2
Refer to the exhibits.
Exhibit 1
Exhibit 2
The IP phone in the exhibit is set up to complete 802.1x authentication to the network. How can the netwotk administrator prevent a user on the computer from receiving network access without authentication?
A. Enable static mode port security on interface 1.
B. Set an 802.1X client limit of 2 on interface 1.
C. Set up the MAC filter on interface 1.
D. Enable MAC-based VLANs on interface 1.
Answer: B
NEW QUESTION: 3
A system administrator is designing an active-active, high availability configuration to WebSphere Message Broker using two nodes. Which are the minimum components required in each node for this configuration?
A. 1 active queue manager, 1 passive queue manager, 1 active broker and 1 passive broker
B. 2 active queue managers, 1 active broker and 1 passive broker
C. 2 active queue managers and 2 active brokers
D. 1 active queue manager, 1 passive queue manager and 2 active brokers
Answer: A
NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 74 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderjd , order_date , ordercustomerid, order status}
Columns of orderjtems table : (order_item_td , order_item_order_id ,
order_item_product_id,
order_item_quantity,order_item_subtotal,order_item_product_price)
Please accomplish following activities.
1. Copy "retaildb.orders" and "retaildb.orderjtems" table to hdfs in respective directory p89_orders and p89_order_items .
2. Join these data using orderjd in Spark and Python
3. Now fetch selected columns from joined data Orderld, Order date and amount collected on this order.
4. Calculate total order placed for each date, and produced the output sorted by date.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=orders --target-dir=p89_orders - -m1 sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=order_items ~target-dir=p89_ order items -m 1
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the
MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoopfs
-cat p89_orders/part-m-00000 hadoop fs -cat p89_order_items/part-m-00000
Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile("p89_orders") orderitems = sc.textFile("p89_order_items")
Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)
#First value is orderjd
ordersKeyValue = orders.map(lambda line: (int(line.split(",")[0]), line))
#Second value as an Orderjd
orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(",")[1]), line))
Step 5 : Join both the RDD using orderjd
joinedData = orderltemsKeyValue.join(ordersKeyValue)
#print the joined data
tor line in joinedData.collect():
print(line)
Format of joinedData as below.
[Orderld, 'All columns from orderltemsKeyValue', 'All columns from orders Key Value']
Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.
revenuePerOrderPerDay = joinedData.map(lambda row: (row[0]( row[1][1].split(",")[1]( f!oat(row[1][0].split('\M}[4]}}}
#printthe result
for line in revenuePerOrderPerDay.collect():
print(line)
Step 7 : Select distinct order ids for each date.
#distinct(date,order_id)
distinctOrdersDate = joinedData.map(lambda row: row[1][1].split('\")[1] + "," + str(row[0])).distinct() for line in distinctOrdersDate.collect(): print(line)
Step 8 : Similar to word count, generate (date, 1) record for each row. newLineTuple = distinctOrdersDate.map(lambda line: (line.split(",")[0], 1))
Step 9 : Do the count for each key(date), to get total order per date. totalOrdersPerDate = newLineTuple.reduceByKey(lambda a, b: a + b}
#print results
for line in totalOrdersPerDate.collect():
print(line)
step 10 : Sort the results by date sortedData=totalOrdersPerDate.sortByKey().collect()
#print results
for line in sortedData:
print(line)
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